Xie Jiacheng, Shao Hua-Chieh, Li Yunxiang, Yan Shunyu, Shen Chenyang, Wang Jing, Zhang You
ArXiv. 2025 Jun 25:arXiv:2503.09978v2.
Deformable liver motion tracking using a single X-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion model-based framework for accurate and robust liver motion tracking from arbitrarily angled single X-ray projections (PCD-Liver), which estimates volumetric liver motion by solving deformable vector fields (DVFs) of a prior liver surface point cloud based on a single X-ray image. The model is patient-specific and consists of two main components: a rigid alignment model to estimate the liver's overall shifts and a conditional point cloud diffusion model that further corrects for liver surface deformations. Conditioned on motion-encoded features extracted from a single X-ray projection via a geometry-informed feature pooling layer, the diffusion model iteratively solves detailed liver surface DVFs in a projection angle-agnostic manner. The liver surface motion estimated by PCD-Liver serves as a boundary condition for a U-Net-based biomechanical model to infer internal liver motion and localize liver tumors. A dataset of ten liver cancer patients was used for evaluation. The accuracy of liver point cloud motion estimation was assessed using root mean square error (RMSE) and 95th-percentile Hausdorff distance (HD95), while liver tumor localization error was quantified using center-of-mass error (COME). The mean (standard deviation) RMSE, HD95, and COME of the prior liver or tumor before motion estimation were 8.82(3.58) mm, 10.84(4.55) mm, and 9.72(4.34) mm, respectively. After PCD-Liver motion estimation, the corresponding values improved to 3.63(1.88) mm, 4.29(1.75) mm, and 3.46(2.15) mm. Under highly noisy conditions, PCD-Liver maintained stable performance. This study presents an accurate and robust framework for deformable liver motion estimation and tumor localization in image-guided radiotherapy.
使用单X射线投影的可变形肝脏运动跟踪能够实现实时运动监测和治疗干预。我们引入了一种基于条件点云扩散模型的框架，用于从任意角度的单X射线投影中进行准确且稳健的肝脏运动跟踪（PCD-Liver），该框架通过基于单X射线图像求解先前肝脏表面点云的可变形矢量场（DVF）来估计肝脏的体积运动。该模型是针对特定患者的，由两个主要部分组成：一个用于估计肝脏整体位移的刚性对齐模型和一个进一步校正肝脏表面变形的条件点云扩散模型。基于通过几何信息特征池化层从单X射线投影中提取的运动编码特征，扩散模型以与投影角度无关的方式迭代求解详细的肝脏表面DVF。PCD-Liver估计的肝脏表面运动作为基于U-Net的生物力学模型的边界条件，以推断肝脏内部运动并定位肝脏肿瘤。使用了一个包含十名肝癌患者的数据集进行评估。使用均方根误差（RMSE）和第95百分位数豪斯多夫距离（HD95）评估肝脏点云运动估计的准确性，而使用质心误差（COME）对肝脏肿瘤定位误差进行量化。运动估计前先前肝脏或肿瘤的平均（标准差）RMSE、HD95和COME分别为8.82（3.58）mm、10.84（4.55）mm和9.72（4.34）mm。经过PCD-Liver运动估计后，相应的值分别提高到3.63（1.88）mm、4.29（1.75）mm和3.46（2.15）mm。在高噪声条件下，PCD-Liver保持稳定性能。本研究提出了一种用于图像引导放射治疗中可变形肝脏运动估计和肿瘤定位的准确且稳健的框架。
